Improved adaptive sliding mode control for a class of uncertain nonlinear systems subjected to input nonlinearity via fuzzy neural networks
type: Item
instance of: scholarly article
title: Improved adaptive sliding mode control for a class of uncertain nonlinear systems subjected to input nonlinearity via fuzzy neural networks
zbMATH Open document ID: 1394.93161
DOI: 10.1155/2015/351524
author: Tat-Bao-Thien Nguyen
author: Teh-Lu Liao
author: Jun-Juh Yan
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: The paper presents an improved adaptive sliding mode control method based on fuzzy neural networks for a class of nonlinear systems subjected to input nonlinearity with unknown model dynamics. The control scheme consists of the modified adaptive and the compensation controllers. The modified adaptive controller online approximates the unknown model dynamics and input nonlinearity and then constructs the sliding mode control law, while the compensation controller takes into account the approximation errors and keeps the system robust. Based on Lyapunov stability theorem, the proposed method can guarantee the asymptotic convergence to zero of the tracking error and provide the robust stability for the closed-loop system. In addition, due to the modification in controller design, the singularity problem that usually appears in indirect adaptive control techniques based on fuzzy/neural approximations is completely eliminated. Finally, the simulation results performed on an inverted pendulum system demonstrate the advanced functions and feasibility of the proposed adaptive control approach.
Mathematics Subject Classification ID: 93C42
Mathematics Subject Classification ID: 93B12
Mathematics Subject Classification ID: 93C40
zbMATH DE Number: 6926124
Wikidata QID: Q59118125
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/351524
OpenAlex ID: W2019798228
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description: scientific article; zbMATH DE number 6926124